Scholars from a range of disciplines interrogate terms relevant to critical studies of big data, from abuse and aggregate to visualization and vulnerability.
This groundbreaking work offers an interdisciplinary perspective on big data and the archives they accrue, interrogating key terms. Scholars from a range of disciplines analyze concepts relevant to critical studies of big data, arranged glossary style—from abuse and aggregate to visualization and vulnerability. They not only challenge conventional usage of such familiar terms as prediction and objectivity but also introduce such unfamiliar ones as overfitting and copynorm. The contributors include a broad range of leading and agenda-setting scholars, including as N. Katherine Hayles, Wendy Hui Kyong Chun, Johanna Drucker, Lisa Gitelman, Safiya Noble, Sarah T. Roberts and Nicole Starosielski.
Uncertainty is inherent to archival practices; the archive as a site of knowledge is fraught with unknowns, errors, and vulnerabilities that are present, and perhaps even amplified, in big data regimes. Bringing lessons from the study of the archive to bear on big data, the contributors consider the broader implications of big data's large-scale determination of knowledge.
Catherine D'Ignazio is Assistant Professor of Urban Science and Planning at MIT and coauthor of Data Feminism (MIT Press).
Nanna Bonde Thylstrup is Associate Professor at Copenhagen Business School and author of The Politics of Mass Digitization (MIT Press).
Daniela Agostinho is Assistant Professor at the Department of Arts and Cultural Studies at the University of Copenhagen. She is coeditor of Uncertain Archives: Critical Keywords for Big Data (MIT Press).
Annie Ring is Associate Professor in the School of European Languages, Culture, and Society at University College London, and author of After the Stasi.
Kristin Veel is Associate Professor at the Department for Arts and Cultural Studies at the University of Copenhagen and coauthor of Tower to Tower (MIT Press).